Current Loss Reserving Developments CAS Annual Meeting November 15, 2005 Chuck Emma, Pinnacle Tom Ryan, Milliman John J. Kollar, ISO

ISO Study of Loss and Loss Adjustment Expense Reserves A. Industry Schedule P (Net) B. Analysis of Direct Losses CAS Annual Meeting November 15, 2005 John J. Kollar, ISO

A. Industry Loss Reserve Analysis More than 900 insurer groups Year-ended 12/31/04 Schedule P data compiled by A. M. Best More than 95% of LLAE reserves for studied lines

Lines studied PP Auto Liability HO/Farmowners Com. Auto Liability Other & Products Liab. Claims-Made Other Liab. Occurrence Com. Multi-Peril Med Mal Occurrence Med Mal Claims-Made Products Occurrence Reinsurance (Non- Proportional Liability) Workers Compensation

Some Key Points Excludes reserves for environmental and asbestos (E&A) claims –Possibly $30B to $50B deficient Analysis assumes incurred losses from 9/11 were fully developed at year-end 2004 –Estimated direct insured losses: $20B to $30B –U.S. net insured losses: $6B to $9B Adjustments have been made for other major catastrophes

Methodology Paid link-ratio technique Case-incurred link-ratio technique Consistent with ISO study of 2003 data

Factors affecting analysis Data quality Development factors Tail factors Professional judgment

Conventions Each deficiency/redundancy expressed as percentage of indicated undiscounted reserve as estimated by ISO –Positive percentages indicate deficiencies –Negative percentages indicate redundancies

Summary Preliminary Indications of Reserve Deficiencies Paid Case Incurred Lines Studied+ 2% + 7% All Other Lines+ 3% + 3% Total – all lines+ 2% + 7% In Dollars$9B$31B –(excluding E & A )

Perspective Reserve adequacy has improved for 3 consecutive years –Reserves were about 3 percentage points more adequate at year-end 2004 than at year-end 2003 –Reserves were about 11 percentage points more adequate at year-end 2004 than at year-end 2001

Preliminary Indications by Line Lines with deficiencies Paid Case Inc. Products Occurrence + 7%+12% Com. Multi-Peril+ 1%+ 4% Workers Comp+ 9%+15% Reinsurance (Non-Prop.) +25% +33%

Preliminary Indications by Line Other Lines PaidCase Inc. Priv. Pass. Auto Liability- 4%- 6% Homeowners/Farmowners-16%-10% Commercial Auto Liability-10%- 1% Other Liability Occurrence- 3%+ 5% Claims Made Other & Prod.- 8%+ 1% Medical Malpractice – Occ.*- 3%+12% Medical Malpractice – C-M.*-11%- 9% * ISO still investigating whether reserve adequacy is overstated consequent to data anomalies.

LALAE Ratios: Accident Year vs. Calendar Year Reserve adequacy deteriorated for at least 6 years but then improved in 2002, 2003 & 2004.

Loss Reserve Changes vs. Industry Profitability, All Lines Changes in reserves are correlated with profitability.

Retrospective Estimated Deficiencies & Economic Discount, All Studied Lines Discounted reserves were inadequate from 2000 to 2003.

B. Analysis of Direct Losses Segment Analysis –State/coverage/class group/etc. Reserving/Benchmarking –Comparable mix of business Tail Factors Confidence Intervals

Schedule P Lines with Homeowners/Farmowners - Homeowners Private Passenger Auto Liability/Medical Commercial Auto/Truck Liability/Medical Commercial Multiple Peril - Commercial Multiple Peril Liability - Commercial Multiple Peril Property ISO Distributions

Schedule P Lines with Medical Malpractice - Occurrence - Hospitals - Physicians - Surgeons Other Liability - Occurrence Products Liability - Occurrence Auto Physical Damage - Commercial Auto Physical Damage - Private Passenger Auto Physical Damage ISO Distributions (Cont’d)

Schedule P Lines with ISO Distributions (Cont’d) Special Property - Fire - Allied Lines - Inland Marine

Segment Analysis – Link Ratios The Schedule P data is net, includes Composite Rated Risks (CRR), and is evaluated as of 12, 24, etc. months. The ISO data is direct, excludes CRR (except as noted), and is evaluated as of 15, 27, etc. months.

Reserving/Benchmarking Used aggregate direct data by segment –State/coverage/class/etc. –Paid/incurred/losses/claim counts/LAE Weighted aggregate direct data by each insurer’s unique mix of business –Losses rather than link ratios –Separately for each accident year Applied link ratios based on aggregate data to an insurer’s data to calculate an indicated reserve

Triangles, Link Ratios, Averages Triangle of developing losses Link Ratio Factors Link Ratio Averages

Benchmarking of Reserves

Tail Factors Modified Bondy Method The ultimate factor (UF) determined using the first prior factor (FF) and the second prior factor (SF) as follows: If SF > 1 and [ 0.8 * LN(SF) >= LN(FF) >= 0 ] or SF < 1 and [ 0.8 * LN(SF) <= LN(FF) <= 0 ] then UF = FF ^ { LN(FF) / [ LN(SF) – LN(FF) ] } Otherwise, UF = FF ^ 4 Development beyond 10 years – up to 20 years using direct data

Development 10 vs. 20 Years

Relative Volatility of Tail Factors

Development of Parameters for Confidence Intervals By line By settlement lag (valuation) Industry for severity By insurer size for frequency Estimated parameters for claim severity and frequency separately

Reserve Risk: Average size and volatility of open claims increases over time

Development of an Insurer’s Confidence Intervals An insurer’s loss and loss adjustment expense reserves - By line - By accident year (latest valuation) Reinsurance arrangements - Retention - Coinsurance - Per claim limit Scale factors to reflect differences in average severity

Measures of Variability Frequency/Severity/Reserves Measure of variability in aggregate reserves Measure of variability in frequencies Measure of variability in severities Measure of variability in reserves by line and period

Confidence Interval and Aggregate Loss Reserve Distribution 50% Confidence Interval Expected Loss

Confidence Intervals for Loss Reserves – Effect of Reinsurance

Confidence Intervals for Loss Reserves – Effect of Size * A mix of Other Liability and Products Liability.

Applications Reserving triangles –Combine segments using each insurer’s unique mix of business Individual segment analysis Tail factors Confidence intervals (ranges around expected) Benchmarking for CEO/CFO, Board, investors, rating agencies, regulators, etc.

Future Plans Explore ways of improving reserve estimates –Credibility weighing an insurer’s data with larger data sets –Generation of more refined confidence intervals –Additional tail factor treatments –Correlations between lines/dependencies What else would be valuable for loss reserving?